Тип публикации: статья из журнала

Год издания: 2023

Идентификатор DOI: 10.3390/math11132937

Аннотация: <jats:p>In this study, parameter adaptation methods for differential evolution are automatically designed using a surrogate approach. In particular, Taylor series are applied to model the searched dependence between the algorithm's parameters and values, describing the current algorithm state. To find the best-performing adaptationПоказать полностьюtechnique, efficient global optimization, a surrogate-assisted optimization technique, is applied. Three parameters are considered: scaling factor, crossover rate and population decrease rate. The learning phase is performed on a set of benchmark problems from the CEC 2017 competition, and the resulting parameter adaptation heuristics are additionally tested on CEC 2022 and SOCO benchmark suites. The results show that the proposed approach is capable of finding efficient adaptation techniques given relatively small computational resources.</jats:p>

Журнал: Mathematics

Выпуск журнала: Т.11, №13

Номера страниц: 2937

ISSN журнала: 22277390

Место издания: Basel

Издатель: MDPI

• Stanovov Vladimir (Institute of Informatics and Telecommunication, Reshetnev Siberian State University of Science and Technology, 660037 Krasnoyarsk, Russia)
• Semenkin Eugene (Institute of Informatics and Telecommunication, Reshetnev Siberian State University of Science and Technology, 660037 Krasnoyarsk, Russia)

• Ядро РИНЦ (eLIBRARY.RU)